Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "45" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 18 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 18 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.564583 | 2.664682 | 1.121918 | 0.985755 | -0.084537 | 1.910723 | 0.193607 | 4.086138 | 0.6307 | 0.6412 | 0.3855 | nan | nan |
| 2459994 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.009102 | 5.086458 | 0.410353 | 0.949313 | -0.479245 | 1.682190 | -0.334875 | 13.984786 | 0.6208 | 0.6263 | 0.3679 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.217316 | 5.346282 | 0.294065 | 0.894414 | -0.720051 | 2.134940 | 0.068782 | 14.805736 | 0.6203 | 0.6222 | 0.3722 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.159897 | 4.026776 | 0.292394 | 0.909800 | -0.025123 | 1.945871 | 2.967751 | 16.810716 | 0.6251 | 0.6316 | 0.3737 | nan | nan |
| 2459989 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.169484 | 3.354858 | 0.248062 | 1.031948 | -0.478895 | 1.691264 | 0.934086 | 11.323607 | 0.6216 | 0.6299 | 0.3780 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.052390 | 4.315420 | 0.226573 | 0.810574 | -0.651442 | 1.192248 | -0.330773 | 10.620385 | 0.6165 | 0.6233 | 0.3684 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.010036 | 2.168077 | 0.276562 | 0.869148 | -0.445864 | 1.788101 | 0.560239 | 17.871980 | 0.6280 | 0.6353 | 0.3666 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.109357 | 4.642249 | 0.326163 | 0.802724 | -0.439831 | 1.879915 | 1.169368 | 12.474192 | 0.6374 | 0.6471 | 0.3248 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.006988 | 4.538512 | 0.356730 | 0.742498 | -0.521681 | 1.949686 | 1.694641 | 15.962441 | 0.6197 | 0.6225 | 0.3688 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.116993 | 1.070145 | 0.397513 | 0.605968 | -0.163393 | 0.407666 | 0.009701 | 1.584857 | 0.6376 | 0.6477 | 0.3540 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.917000 | 3.548655 | 0.339293 | 0.558107 | -0.849591 | 1.097197 | -0.097160 | 1.954688 | 0.6476 | 0.6643 | 0.3065 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.088745 | 2.574757 | 0.413977 | 0.726294 | -0.328763 | 0.727828 | 0.380168 | 1.023747 | 0.6946 | 0.6930 | 0.2800 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.498658 | 3.405025 | 0.209486 | 0.580574 | -0.850542 | 1.465101 | 0.016223 | 1.776834 | 0.6188 | 0.6282 | 0.3657 | nan | nan |
| 2459980 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.241148 | 4.209744 | 0.115354 | 0.563956 | -0.761605 | 1.372413 | 0.436512 | 1.287137 | 0.6674 | 0.6740 | 0.2961 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.627124 | 3.235619 | -0.036062 | 0.559355 | -0.717126 | 1.466547 | 0.270634 | 1.574564 | 0.6102 | 0.6231 | 0.3671 | nan | nan |
| 2459978 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.507894 | 3.100999 | 0.024403 | 0.562776 | -0.585641 | 1.030731 | 1.043193 | 2.717875 | 0.6110 | 0.6227 | 0.3740 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.384368 | 3.693934 | 0.101658 | 0.534004 | -0.728058 | 2.132975 | 0.018456 | 2.544051 | 0.5780 | 0.5903 | 0.3350 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.611824 | 2.882611 | 0.134869 | 0.555254 | -0.466169 | 2.088615 | 0.374371 | 2.198790 | 0.6240 | 0.6346 | 0.3651 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 4.086138 | 0.564583 | 2.664682 | 1.121918 | 0.985755 | -0.084537 | 1.910723 | 0.193607 | 4.086138 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 13.984786 | 0.009102 | 5.086458 | 0.410353 | 0.949313 | -0.479245 | 1.682190 | -0.334875 | 13.984786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 14.805736 | 0.217316 | 5.346282 | 0.294065 | 0.894414 | -0.720051 | 2.134940 | 0.068782 | 14.805736 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 16.810716 | 4.026776 | 0.159897 | 0.909800 | 0.292394 | 1.945871 | -0.025123 | 16.810716 | 2.967751 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 11.323607 | 3.354858 | 0.169484 | 1.031948 | 0.248062 | 1.691264 | -0.478895 | 11.323607 | 0.934086 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 10.620385 | 4.315420 | 0.052390 | 0.810574 | 0.226573 | 1.192248 | -0.651442 | 10.620385 | -0.330773 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 17.871980 | 0.010036 | 2.168077 | 0.276562 | 0.869148 | -0.445864 | 1.788101 | 0.560239 | 17.871980 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 12.474192 | 4.642249 | -0.109357 | 0.802724 | 0.326163 | 1.879915 | -0.439831 | 12.474192 | 1.169368 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 15.962441 | 4.538512 | 0.006988 | 0.742498 | 0.356730 | 1.949686 | -0.521681 | 15.962441 | 1.694641 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Temporal Discontinuties | 1.584857 | -0.116993 | 1.070145 | 0.397513 | 0.605968 | -0.163393 | 0.407666 | 0.009701 | 1.584857 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 3.548655 | -0.917000 | 3.548655 | 0.339293 | 0.558107 | -0.849591 | 1.097197 | -0.097160 | 1.954688 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 2.574757 | 0.088745 | 2.574757 | 0.413977 | 0.726294 | -0.328763 | 0.727828 | 0.380168 | 1.023747 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 3.405025 | 3.405025 | -0.498658 | 0.580574 | 0.209486 | 1.465101 | -0.850542 | 1.776834 | 0.016223 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 4.209744 | 4.209744 | -0.241148 | 0.563956 | 0.115354 | 1.372413 | -0.761605 | 1.287137 | 0.436512 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 3.235619 | -0.627124 | 3.235619 | -0.036062 | 0.559355 | -0.717126 | 1.466547 | 0.270634 | 1.574564 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 3.100999 | 3.100999 | -0.507894 | 0.562776 | 0.024403 | 1.030731 | -0.585641 | 2.717875 | 1.043193 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 3.693934 | -0.384368 | 3.693934 | 0.101658 | 0.534004 | -0.728058 | 2.132975 | 0.018456 | 2.544051 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | N05 | digital_ok | nn Shape | 2.882611 | 2.882611 | -0.611824 | 0.555254 | 0.134869 | 2.088615 | -0.466169 | 2.198790 | 0.374371 |